Expense_Project_ML

🧾 Monthly Expense Prediction System (Machine Learning)

📌 Overview

This project is a Machine Learning-based Expense Prediction System that predicts future monthly expenses based on historical data. It uses Linear Regression to identify trends and forecast upcoming expenses.

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🚀 Features

• 📊 Predicts next month's expense using past data
• 📈 Visualizes data with a regression line
• 🟢 Highlights future prediction on graph
• 🔁 Uses synthetic large dataset with random variation
• 📉 Calculates model error (MSE)

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🧠 Technologies Used

• Python
• Pandas
• NumPy
• Scikit-learn
• Matplotlib

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📂 Project Structure

Expense_Project/
│
├── data.csv              # Dataset
├── generate_data.py      # Script to generate large dataset
├── model.py              # ML model (Linear Regression)
├── main.py               # Main execution file
├── plot.py               # Graph visualization
├── requirements.txt      # Dependencies

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⚙️ How It Works

1. Data Generation

   • Synthetic data is generated with a trend + random variation
   • Simulates real-world expense behavior

2. Model Training

   • Linear Regression learns the relationship between:

     • Month (input)
     • Expense (output)

3. Prediction

   • Model predicts future expense using learned trend

4. Visualization

   • Blue dots → Actual data
   • Red line → Regression trend
   • Green dot → Future prediction

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▶️ How to Run

Step 1: Clone the repository

git clone https://github.com/your-username/Expense_Prediction_Project.git
cd Expense_Prediction_Project

Step 2: Install dependencies

pip install -r requirements.txt

Step 3: Generate dataset

python generate_data.py

Step 4: Run the project

python main.py

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📊 Sample Output

• Predicted Expense for Next Month
• Graph showing trend and prediction

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📉 Model Evaluation

• Mean Squared Error (MSE) is used to measure model performance

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💡 Use Cases

• Personal finance planning
• Budget forecasting
• Expense trend analysis

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🔥 Future Improvements

• Add multiple features (income, savings, etc.)
• Use advanced models (Polynomial Regression)
• Build web interface (Streamlit)
• Real-time data integration

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👨‍💻 Author

Sandeep Choudhary B.Tech AI & Data Science Arya College of Engineering & IT, Jaipur

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⭐ If you like this project

Give it a star ⭐ on GitHub!